boltz2-nim

Boltz2 NIM

Predict biomolecular structures and optional ligand affinity. Use this SKILL.md for first-pass hosted/local usage; load supplemental files only when needed:

• references/api.md: exact endpoints, schemas, Docker flags, response fields.
• references/science.md: purpose, strengths, limitations, and handoffs.
• references/parameters.md: prediction, sampling, MSA, template, affinity tuning.
• references/validation.md: mmCIF, confidence, affinity, and chemistry checks.
• references/examples.md: compact hosted/local payload patterns.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted: https://health.api.nvidia.com/v1/biology/mit/boltz2/predict
• Local: http://localhost:8000/biology/mit/boltz2/predict

Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Local Docker

For local setup answers, copy the preflight below before docker login, docker run, readiness, and the no-auth local request. Do not invent a cache default or drop the .env load or NVIDIA_API_KEY fallback.

set -a
[ -f .env ] && . ./.env
set +a

if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"

docker run --rm --name boltz2 --gpus all \
  --shm-size=16G \
  -e NGC_API_KEY \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/mit/boltz2:1.6.0

Readiness:

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

First startup downloads about 30 GB of model weights.

Request Pattern

import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/mit/boltz2/predict"
    if HOSTED else "http://localhost:8000/biology/mit/boltz2/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"

payload = {
    "polymers": [{
        "id": "A",
        "molecule_type": "protein",
        "sequence": "MTEYKLVVVGACGVGKSALTIQLIQNHFVDEYDPT",
    }],
    "recycling_steps": 3,
    "sampling_steps": 50,
    "diffusion_samples": 1,
    "step_scale": 1.638,
    "output_format": "mmcif",
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

Payload essentials:

• Protein polymer: {"molecule_type": "protein", "sequence": "..."}.
• DNA/RNA polymer: add another polymer with molecule_type "dna" or "rna".
• Ligand by SMILES: {"id": "L1", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"}.
• Ligand by CCD: {"id": "L1", "ccd": "ATP"}.
• Affinity: set "predict_affinity": True on exactly one ligand; report affinity_pic50, affinity_pred_value, and affinity_probability_binary.
• Precomputed A3M MSA goes under the protein polymer. The A3M record uses alignment, format, and rank; do not use a stale data field.

protein_with_msa = {
    "id": "A",
    "molecule_type": "protein",
    "sequence": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
    "msa": {"msa_search": {"a3m": {
        "alignment": ">query\nMTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT",
        "format": "a3m",
        "rank": 0,
    }}},
}

Save And Report Output

for i, structure in enumerate(result["structures"], start=1):
    with open(f"structure_{i}.cif", "w", encoding="utf-8") as handle:
        handle.write(structure["structure"])
for i, score in enumerate(result.get("confidence_scores", []), start=1):
    print(f"structure {i} confidence {score:.4f}")
if "affinities" in result:
    for ligand_id, aff in result["affinities"].items():
        print(ligand_id, aff["affinity_pic50"][0], aff["affinity_pred_value"][0], aff["affinity_probability_binary"][0])

Save every .cif artifact. Visualize in PyMOL, ChimeraX, or UCSF Chimera. For confidence/affinity sanity checks, read references/validation.md.

Limits And Troubleshooting

• Polymers/request: 12. Ligands/request: 20. Chain length: 4096 residues.
• Affinity prediction supports one ligand per request and adds runtime.
• 422: invalid sequence, invalid CCD/SMILES, malformed MSA, or multiple affinity ligands.
• Local URL/auth: local path has no hosted auth header; wait on /v1/health/ready.
• Local startup: use --gpus all, --shm-size=16G, and the /opt/nim/.cache mount.